Identification of objects such as vehicles using computer-implemented image analysis is used in various applications. For example, license plate image recognition systems are frequently used in automated tollgates that identify moving vehicles to facilitate collecting tolls. In addition, systems that combine license plate recognition and detection of vehicle features, such as car color or make, are used to improve identification accuracy when the license plate cannot be adequately identified. For example, highly accurate vehicle identification based only on vehicle features is possible with techniques based on machine learning algorithms such as convolutional neural networks. Such algorithms capture features from images of known vehicles and then identify an unknown vehicle in an image by correlating image features. Although such machine learning techniques may be computationally intensive, they can improve identification accuracy and facilitate the automation of a vehicle image identification system.
Traditionally, image recognition of vehicles has focused on non-portable applications, such as the above-noted toll collection, monitoring the entrance of a parking lot, or supervising highway entrances. However, identification of vehicles using mobile or handheld devices is highly desirable for portable applications. For example, when a person sees a vehicle in the street and wants information about it, the person would normally need to approach the vehicle to determine details such as make or model. Alternatively, the person may need to browse multiple images and websites to try to find vehicle characteristics. Portable identification of vehicles using image analysis may facilitate the identification of vehicles without the burden of approaching the vehicle or perform later searches.